Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices

BMJ Open, vol. 13, pp. e066183

Abstract

OBJECTIVE: The present study aimed to early identify patients with persistent somatic symptoms (PSS) in primary care by exploring routine care data-based approaches. DESIGN/SETTING: A cohort study based on routine primary care data from 76 general practices in the Netherlands was executed for predictive modelling. PARTICIPANTS: Inclusion of 94 440 adult patients was based on: at least 7-year general practice enrolment, having more than one symptom/disease registration and >10 consultations. METHODS: Cases were selected based on the first PSS registration in 2017-2018. Candidate predictors were selected 2-5 years prior to PSS and categorised into data-driven approaches: symptoms/diseases, medications, referrals, sequential patterns and changing lab results; and theory-driven approaches: constructed factors based on literature and terminology in free text. Of these, 12 candidate predictor categories were formed and used to develop prediction models by cross-validated least absolute shrinkage and selection operator regression on 80% of the dataset. Derived models were internally validated on the remaining 20% of the dataset. RESULTS: All models had comparable predictive values (area under the receiver operating characteristic curves=0.70 to 0.72). Predictors are related to genital complaints, specific symptoms (eg, digestive, fatigue and mood), healthcare utilisation, and number of complaints. Most fruitful predictor categories are literature-based and medications. Predictors often had overlapping constructs, such as digestive symptoms (symptom/disease codes) and drugs for anti-constipation (medication codes), indicating that registration is inconsistent between general practitioners (GPs). CONCLUSIONS: The findings indicate low to moderate diagnostic accuracy for early identification of PSS based on routine primary care data. Nonetheless, simple clinical decision rules based on structured symptom/disease or medication codes could possibly be an efficient way to support GPs in identifying patients at risk of PSS. A full data-based prediction currently appears to be hampered by inconsistent and missing registrations. Future research on predictive modelling of PSS using routine care data should focus on data enrichment or free-text mining to overcome inconsistent registrations and improve predictive accuracy.

Authors 7

1. Willeke M. Kitselaar corresponding Aachen Health Campus The Hague/Department of Public Health and Primary Care Health Medical and Neuropsychology Unit Department of Psychology, Leiden University

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Health Campus The Hague/Department of Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands

   Health, Medical and Neuropsychology unit, Department of Psychology, Leiden University, Leiden, Netherlands

2. Frederike L. Büchner Aachen Health Campus The Hague/Department of Public Health and Primary Care

   Leiden University Medical Center

   Affiliation as printed

   Health Campus The Hague/Department of Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands

3. Rosalie van der Vaart Aachen Health Medical and Neuropsychology Unit Department of Psychology, Leiden University

   Leiden University

   Affiliation as printed

   Health, Medical and Neuropsychology unit, Department of Psychology, Leiden University, Leiden, Netherlands

4. Stephen P. Sutch Aachen Health Campus The Hague/Department of Public Health and Primary Care

   Johns Hopkins University · Leiden University Medical Center

   Affiliation as printed

   HSR, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA

   Health Campus The Hague/Department of Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands

5. Frank C. Bennis

   Vrije Universiteit Amsterdam · Netherlands Institute for Health Services Research

   Affiliation as printed

   Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Netherlands Institute for Health Services Research, Utrecht, Netherlands

6. Andrea W.M. Evers Aachen Health Medical and Neuropsychology Unit Department of Psychology, Leiden University

   Leiden University

   Affiliation as printed

   Health, Medical and Neuropsychology unit, Department of Psychology, Leiden University, Leiden, Netherlands

7. Mattijs Everard Numans Aachen Health Campus The Hague/Department of Public Health and Primary Care

   Leiden University Medical Center

   Affiliation as printed

   Health Campus The Hague/Department of Public Health and Primary Care, Leiden University Medical Center, The Hague, The Netherlands